Research Summary

The research in our computational biology and bionformatics laboratory involves analysis of genomics and proteomics experiments. This includes computational analysis of output from high-throughput datasets generated from experiments involving melanoma, breast cancer, hematopoeisis, cell cycle genomics, and protein-protein interactions. The central focus of our earlier studies was to reveal functional and regulatory gene modules using genome-wide data generated in various "Omics" experiments and auxiliary information from genomics databases. We addressed issues of normalization and artifacts in microarrays. Subsequently, we developed a novel spectral method for bi-directional clustering of cancer microarray data to reveal regulatory gene modules. The lab has also focused on extracting meaningful biological information from experimental systems by assessing the co-expression of genes regulated by various transcription factors, evaluating pathway expression and building genetic networks based on functionality rather than pure expression. This approach is a step forward in identifying genes in regulatory networks that are disrupted by mutations of tumor suppressors and oncogenes and could shed light on the process of malignant transformation. Our research also involves the integration of sequence information with genome-wide transcriptome and epigenome profiles. This analysis has allowed us and our collaborators to reveal non-unique sequence recognition motifs of transcription factors in an in vivo context and to predict combinatorial regulation partners of transcription factors. Moreover, this approach has allowed us to find spatial organization of transcription factor binding events, as well as their relationships with other epigenomic factors.

The current computational activities in our laboratory include the following areas: a) Application of signal processing approaches for identification of relevant biological signals in high-throughput experiments, such as identification of aberrations in multi-subclonal cancer samples, signal denoising in next generation platforms, and de-mixing of cell types in heterogeneous samples, b) developing approaches to analyze high dimensional data from genomics platforms for biomarker discovery and personalized medicine. In particular, we use advanced applied mathematical methods to search complex local and non-local genomic patterns across the genome that may discriminate cancer patients with good vs. poor outcomes in CNA studies employing next generation sequencing or SNP platforms and c) uncovering direct and collective regulatory relationships between regulators (TFs, epigenomic factors and miRNAs) and their target genes by integration of heterogeneous Omics datasets and DNA sequences.

From a biological standpoint we are particularly interested in: a) Identification of primary or drug-treated metastatic subclones with proliferation and invasion potential in heterogeneous cancer biopsies b) The interplay between regulatory motifs, chromatin status and multi scale chromosomal structure c) Determining whether complex traits associated with certain common diseases vary across populations with different genetic backgrounds

Extensive Research Description

The research in our
computational biology and bionformatics laboratory involves analysis of
genomics and proteomics experiments. This includes computational analysis of
output from high-throughput datasets generated from experiments involving
melanoma, breast cancer, hematopoeisis, cell cycle genomics, and
protein-protein interactions. The central focus of our earlier studies was to
reveal functional and regulatory gene modules using genome-wide data generated
in various "Omics" experiments and auxiliary information from
genomics databases. We addressed issues of normalization and artifacts in
microarrays. Subsequently, we developed a novel spectral method for
bi-directional clustering of cancer microarray data to reveal regulatory gene
modules. The lab has also focused on extracting meaningful biological
information from experimental systems by assessing the co-expression of genes
regulated by various transcription factors, evaluating pathway expression and
building genetic networks based on functionality rather than pure
expression. This approach is a step forward in identifying genes in
regulatory networks that are disrupted by mutations of tumor suppressors and
oncogenes and could shed light on the process of malignant
transformation. Our research also involves the integration of sequence
information with genome-wide transcriptome and epigenome profiles. This
analysis has allowed us and our collaborators to reveal non-unique sequence
recognition motifs of transcription factors in an in vivo context and to
predict combinatorial regulation partners of transcription factors. Moreover,
this approach has allowed us to find spatial organization of transcription
factor binding events, as well as their relationships with other epigenomic factors.

The current computational
activities in our laboratory include the following areas: a) Application of
signal processing approaches for identification of relevant biological signals
in high-throughput experiments, such as identification of aberrations in
multi-subclonal cancer samples, signal denoising in next generation
platforms, and de-mixing of cell types in heterogeneous samples, b)
developing approaches to analyze high dimensional data from genomics platforms
for biomarker discovery and personalized medicine. In particular, we use
advanced applied mathematical methods to search complex local and non-local
genomic patterns across the genome that may discriminate cancer patients with
good vs. poor outcomes in CNA studies employing next generation sequencing or
SNP platforms and c) uncovering direct and collective regulatory relationships
between regulators (TFs, epigenomic factors and miRNAs) and their target genes
by integration of heterogeneous Omics datasets and DNA sequences.

From a biological
standpoint we are particularly interested in:
a)
Identification of primary or drug-treated metastatic
subclones with proliferation and invasion potential in heterogeneous cancer
biopsies
b)
The interplay between regulatory motifs, chromatin
status and multi scale chromosomal structure
c)
Determining whether complex traits associated with
certain common diseases vary across populations with different genetic
backgrounds

In silico de-mixing of genomics signals from heterogeneous tumor cell populations into their leading subclonal components (http://arxiv.org/abs/1301.1966)

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